为进一步提高基于声学层析成像的温度分布重建精度,引入了一种偏差较小的误差函数(ERF)对稀疏重构模型进行改进,并使用迭代重加权算法对模型进一步优化,最后采用交替方向乘子算法(ADMM)对模型进行求解,从而完成温度分布重建。进行了仿真与实验测试,并与LASSO、ART及Landweber算法进行比较,仿真实验中基于ERF模型的温度分布重建质量最优,其平均相对误差及均方根误差分别为0.1%和0.14%;实验测试中,其设定温度点重建温度误差绝对值的平均值最小,为0.043%,明显小于其它3种算法。
Abstract
In order to further improve the reconstruction accuracy of temperature distribution based on acoustic tomography, an error function (ERF) with small deviation is introduced to improve the sparse reconstruction model, and the iterative reweighting algorithm is used to further optimize the model. Finally, the alternating direction multiplier algorithm (ADMM) is used to solve the model, so as to complete the reconstruction of temperature distribution. Simulation and experimental tests are carried out and compared with LASSO, ART and Landweber algorithms. In the simulation experiment, the reconstruction quality of temperature distribution based on ERF model is the best, with the average relative error and root mean square error of 0.1% and 0.14% respectively. In the experimental testing, the average absolute value of the reconstruction temperature error at the set temperature point is the smallest, 0.043%, which is significantly smaller than the other three algorithms.
关键词
温度测量 /
声学层析成像 /
稀疏重构 /
误差函数 /
迭代重加权算法 /
交替方向乘子算法
Key words
temperature measurement /
acoustic tomography /
sparse reconstruction /
error function /
iterative reweighting algorithm /
alternating direction method of multipliers algorithm
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基金
国家自然科学基金(61973115,62203172)
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